Crop Yield Forecasting using Machine Learning Techniques - A Systematic Literature Review

Abstract

The utilization of machine learning has become increasingly important in the prediction of crop yields for facilitating decisions regarding crop cultivation and management during the growing season. Numerous machine learning and data mining algorithms have been developed to support research in crop yield forecasting. In this study, a systematic literature review (SLR) was conducted on research published between 2016 and 2021 to investigate the use of machine learning in crop yield forecasting. A total of 261 relevant studies were identified from five electronic databases, out of which 15 studies were selected for further analysis based on inclusion and exclusion criteria. The selected studies were thoroughly examined, and their methods and features were analyzed, to provide suggestions for future research. The results showed that evapotranspiration, temperature, precipitation, and soil type were the most commonly used features in crop yield forecasting, while RMSE, MSE, MAE, and R2 were the most commonly used evaluation parameters. The challenges include selecting appropriate input variables, handling missing data and outliers, and capturing non-linear relationships between variables. The authors discuss various techniques such as feature selection, regularization, imputation, non-linear machine learning, data preprocessing, and data augmentation to address these challenges. The Support Vector Machine, Linear Regression, Artificial Neural Network (ANN), and Long-Short Term Memory (LSTM) were identified as the most commonly used algorithms in these models.